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ADDA > Lecture 2 > Flashcards

Lecture 2 Flashcards

1
Q

What are the different types of psychological research?

A
  • basic (look for knowledge for its own sake; theory) vs. applied (application)
  • lab (control over research) vs. field (realism, face validity, ecological validity)
  • quantitative (stats) vs. qualitative (richness of description)
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2
Q

What are the different experimental designs?

A
  • single factor (2 levels, or more)
  • factorial (2 or more, main effects and interactions)
  • correlational (association)
  • regression (predict)
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3
Q

What is the difference between applied and quasi-experimental research? What is the issue with this?

A
  • applied is controlled, forced random assignment
  • quasi-experimental is when groups occur naturally, no random assignment possible

ISSUE: lose any sense of causality. You have lost control

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4
Q

What are the problems with NHST? What should you do to attempt to remedy this?

A
  • low power: may be a diff you don’t see
  • high power: find even a very small diff as sig
  • Type 1 error: false +ve; accepting false hypothesis as true
  • Type 2 error: false -ve; rejecting true hypothesis as incorrect
  • FIX: report CIs and effect sizes with p values
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5
Q

What are the two types of multivariate models we fit to data?

A
  • confirmatory: seek to test prediction

- exploratory: models that seek to account for rships

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6
Q

What 3 things do you use to assess the fit of a model?

A
  • residuals
  • summary measures (eg. R2)
  • statistical test (eg. null)
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7
Q

What are the different types of variables?

A
  • independent vs. dependent (outcome vs. predictor)
  • discrete vs. continuous (discrete can be categorical or orindal)
  • exogenous vs. endogenous (exo = outside system we’re modelling)
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8
Q

What are the levels of measurement?

A
  • nominal (categorical)
  • ordinal
  • interval
  • ratio
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9
Q

What are the 2 types of missing data?

A
  • missing completely at random (MCAR)
  • missing at random (MAR)
  • both of these are IGNORABLE
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10
Q

What is MCAR?

A
  • missingness not related to any other variable
  • BEST missingness
  • Little’s test (want >.05), don’t reject null that it’s MCAR
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11
Q

What is MAR?

A
  • missingness related to another variable, but no patterns within the variable (not insidious)
  • prob. that missingness is unrelated, after controlling for another variable
  • eg. depressed people less likely to report SES, but within depressed people the prob of reported SES is unrelated to SES level
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12
Q

How should you estimate missing data?

A
  • DO NOT use the mean
  • SPSS Missing Value Analysis: uses regression and EM algorithm approaches
  • SPSS Multiple Imputation. MI creates multiple complete sets of data
  • if a person is missing some items in a scale but has responded to others, then use the data you have to estimate missing data
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13
Q

When do you transform data?

A
  • only if you really have to
  • can transform so that it meets the assumptions of your test
  • can change symmetry of data (i.e. want normal distro.) > can fix skew but not kurtosis
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14
Q

Why use Guttman instead of Alpha?

A
  • alpha not the best
  • all relaibility coefficients are pessimistic (lower bounds - i.e. that value or higher)
  • Guttman allows for choice of lower bound estimates
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15
Q

How do we develop research?

A
  • observations
  • theory
  • past research (gaps)
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16
Q

What are the essential elements of experimental research?

A
  • hold some variables constant
  • vary others (IV)
  • observe changes on another variable (DV)
17
Q

What are within and between subjects designs? What are the main issues with each of these?

A
  • between: independent groups; need equivalent groups (use random sampling and random allocation)
  • within: repeated measures; need to control for sequencing (learning) effects (counterbalance)
18
Q

What is the goal of applied research?

A

to investigate real-world phenomenon

19
Q

What 2 things should you never say?

A
  • X causes Y

- X leads to Y

20
Q

What is multivariate?

A

multiple DVs

21
Q

What is the basic model that underlies everything?

A

data = model + residual

- have a residual for each individual or each cell

22
Q

What does the residual account for?

A
  • random noise
  • measurement error
  • diffs in scales
  • etc.
  • tells us how well the model FITS the data
23
Q

What are the 2 types of NHST in model fit?

A
  • model IS null: want less than .05, reject model/null, residuals large
  • model NOT full: want >.05, do not reject model model, residuals not too large
24
Q

What are the model parameters in the regression equation?

A
  • the regression coefficients/weights
25
Q

How can you represent the regression equation in matrix form? What does regression do?

A

Y = Xb + e

- predicting a vector from a matrix

26
Q

What is the regression equation?

A

Y = b0 + b1X1 + b2X2 + e

27
Q

What does i indicate in the regression equation?

A
  • the model holds for every i

- holds for every person

28
Q

What 4 things do you need to do after you imput your data?

A
  • CHECK the data
  • missing data
  • transformations
  • form totals
29
Q

What are the 2 types of deleting data? Which one is better?

A
  • pairwise: remove pairs of cases relevant to analysis
  • listwise: remove any case with missing data (LOSE POWER!)
  • pairwise is better, listwise loses power
30
Q

What are the steps involved in missing data?

A
  • determine extent
  • delete cases
  • evaluate missing values
31
Q

What are common data transformations?

A

PULL IN HIGHER TAIL

  • (-)1/x2 > strongest change
  • (-)1/x
  • log(x)
  • sqrt(x)

PULL IN LOWER TAIL

  • x2
  • x3
  • antilog(x) > strongest change
32
Q

When is correlational/regressional research helpful?

A
  • when experimental cannot be done
  • cannot randomly assign people to groups
  • has ecological validity
33
Q

When is causality possible?

A
  • carefully designed longitudinal studies

- careful experimental design

34
Q

What type of variables do ANOVA and independent t-tests require?

A

discrete

35
Q

What is interesting about ordinal scales in psychological research?

A

they are often assume to be interval

- this is usually fine, but can sometimes cause issues

36
Q

What do totals involve? (6 things)

A
  • reliability of test scores
  • validity of subscales
  • develop new subscales, if necessary
  • form empirically weighted totals
  • you can drop ‘dud’ items
  • cope with missing data

ADDA (15 decks)

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